Superior Acetone Selectivity in Gas Mixtures by Catalyst‐Filtered Chemoresistive Sensors
Bibliographic record
Abstract
Abstract Acetone is a toxic air pollutant and a key breath marker for non‐invasively monitoring fat metabolism. Its routine detection in realistic gas mixtures (i.e., human breath and indoor air), however, is challenging, as low‐cost acetone sensors suffer from insufficient selectivity. Here, a compact detector for acetone sensing is introduced, having unprecedented selectivity (>250) over the most challenging interferants (e.g., alcohols, aldehydes, aromatics, isoprene, ammonia, H2, and CO). That way, acetone is quantified with fast response (<1 min) down to, at least, 50 parts per billion (ppb) in gas mixtures with such interferants having up to two orders of magnitude higher concentration than acetone at realistic relative humidities (RH = 30–90%). The detector consists of a catalytic packed bed (30 mg) of flame‐made Al2O3 nanoparticles (120 m2 g−1) decorated with Pt nanoclusters (average size 9 nm) and a highly sensitive chemo‐resistive sensor made by flame aerosol deposition and in situ annealing of nanostructured Si‐doped ε‐WO3 (Si/WO3). Most importantly, the catalytic packed bed converts interferants continuously enabling highly selective acetone sensing even in the exhaled breath of a volunteer. The detector exhibits stable performance over, at least, 145 days at 90% RH, as validated by mass spectrometry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".